AI Product Manager Jobs Brazil

AI Product Manager jobs in Brazil on Rex.zone focus on defining AI product strategy and shipping production ML features across LLM, NLP, and computer vision workflows. In this role, you translate business goals into model requirements, dataset needs, and evaluation metrics; partner with engineering, data science, and MLOps on training pipelines; and drive iteration through offline/online experiments and human-in-the-loop feedback (including RLHF, prompt evaluation, and quality assurance). Explore remote, full-time opportunities with AI labs, tech startups, and enterprise teams hiring in Brazil and across global time zones.

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AI Product Manager Jobs Brazil — LinkedIn Job Metadata

Title: AI Product Manager Jobs Brazil | Date: 25-02-2026 | Company: Rexzone | Country: US | Remote Type: Remote | Employment Type: FULL_TIME | Experience Level: Mid-Senior | Industry: Technology | Job Function: Engineering | Skills: AI product management, product strategy, roadmap planning, stakeholder management, machine learning, LLM, NLP, computer vision, MLOps, model evaluation, A/B testing, experimentation, metrics and KPIs, data labeling, RLHF, prompt evaluation | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR

About the Role

You will own the product lifecycle for AI capabilities, from discovery and requirements to launch and iteration. You will define success metrics (offline evaluation and online impact), align cross-functional teams, and operationalize data workflows including training data quality, annotation guidelines compliance, content safety labeling, and human feedback loops such as RLHF and prompt evaluation. You will collaborate with engineering and MLOps to ship reliable inference services, monitor model performance improvement, manage drift and regression, and ensure responsible AI practices.

What You Will Do

Define AI product vision, PRDs, and roadmaps aligned to user problems and business outcomes; translate product goals into model objectives, data requirements, and evaluation plans; partner with ML, NLP, CV, and platform engineers on LLM training pipelines, retrieval, and inference APIs; design evaluation frameworks using QA evaluation, prompt evaluation, and human-in-the-loop feedback (including RLHF) to measure quality and safety; coordinate data labeling programs (taxonomy, labeling instructions, gold sets, audits) to improve training data quality; run experiments including A/B testing, feature flags, and iterative rollouts; manage stakeholder expectations, tradeoffs, and delivery timelines across distributed teams; track product analytics, model monitoring, and error analysis to drive continuous improvement.

Required Qualifications

Mid-Senior experience leading product initiatives with ML or AI components; strong ability to write clear requirements and define measurable success metrics; working knowledge of ML concepts such as classification, ranking, generative models, embeddings, and evaluation; experience collaborating with engineering, data science, and design to ship production features; familiarity with data workflows including data labeling, annotation QA, dataset versioning, and bias analysis; ability to prioritize across roadmap, technical constraints, and operational dependencies.

Preferred Qualifications

Experience with LLM product patterns such as RAG, tool use, prompt orchestration, and guardrails; hands-on understanding of RLHF, prompt evaluation, and content safety labeling; exposure to NLP, computer vision annotation, named entity recognition, or multimodal evaluation; experience with experimentation platforms, feature stores, and MLOps monitoring; background working with AI labs, tech startups, BPOs, or annotation vendors; experience operating in remote, global, or Brazil-based teams.

How Work Happens on Rex.zone

Rex.zone connects candidates to remote AI teams and hiring pipelines. Roles may include full-time, contract, or freelance work across domains like NLP, computer vision, LLM applications, and content safety. You will see job details, required skills, and application routes on Rex.zone, and you can tailor your profile to highlight AI product delivery, model evaluation planning, and data operations leadership.

Frequently Asked Questions

  • Q: What does an AI Product Manager do in practice?

    An AI Product Manager defines the AI feature scope, user experience, and success metrics, then coordinates ML engineering, data science, and MLOps to ship and iterate. This includes dataset requirements, training data quality, evaluation design (offline benchmarks and online experiments), and ongoing monitoring for regressions and drift.

  • Q: Are these AI Product Manager jobs Brazil roles remote?

    Yes. The roles on this page are explicitly marked Remote, and may support distributed collaboration across Brazil and global time zones depending on the team.

  • Q: What AI domains are common for these roles?

    Common domains include LLM applications, NLP, computer vision, and content safety. Many teams also run human-in-the-loop workflows such as data labeling, QA evaluation, prompt evaluation, and RLHF to improve model quality.

  • Q: What skills should I highlight to match AI Product Manager jobs Brazil search intent?

    Highlight AI product management, product strategy, roadmap planning, stakeholder management, machine learning fundamentals, LLM/NLP/CV familiarity, MLOps awareness, model evaluation, experimentation, A/B testing, and data operations concepts like data labeling and RLHF-informed iteration.

  • Q: Do companies expect hands-on ML coding from an AI Product Manager?

    Often not, but you should be comfortable with ML terminology, evaluation metrics, tradeoffs, and data workflows. Some teams prefer candidates who can query data, interpret experiments, and collaborate deeply on model and pipeline constraints.

  • Q: How do I apply via Rex.zone?

    Use Rex.zone to find the AI Product Manager listings that match your background, then follow the application instructions in each job post. Keep your profile focused on shipped AI features, measurable outcomes, and experience with evaluation and data quality processes.

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